Households and NPISHs Final consumption expenditure per capita growth (annual %)

The indicator 'Households and NPISHs Final Consumption Expenditure per capita growth (annual %)' serves as a critical gauge of economic vitality by measuring the changes in real consumer spending per individual within households and Non-Profit Institutions Serving Households (NPISHs). This metric encapsulates the annual percentage growth regarding the consumption expenditure of these entities, reflecting overall economic health and progress in living standards.

This indicator holds great significance as it directly correlates with the purchasing power of households, serving as an essential barometer of economic growth and consumer confidence. Higher growth rates often signal that consumers are more willing to spend, indicating optimism about their financial future, which can lead to increased production and employment within the economy. Conversely, low or negative growth can highlight economic distress, where households cut back on spending and thereby further inhibit economic activity.

In 2023, the median value for this indicator was recorded at 1.69%, showcasing a moderate pace of growth. The top five areas exhibiting robust growth included Turkey at 13.16%, Macao SAR China at 13.06%, Ukraine at 11.59%, Maldives at 10.65%, and Brunei at 10.27%. Such figures underscore a notable recovery or heightened consumer activity in these regions, perhaps driven by factors such as increased wages, tourism activity, or economic reforms promoting consumption.

On the flip side, the bottom five areas—Madagascar at -14.45%, Sudan at -12.33%, Gambia at -6.9%, Palau at -6.48%, and the Marshall Islands at -6.2%—illustrate troubling economic conditions. Negative growth in these regions often results from a combination of political instability, economic crises, or external shocks such as natural disasters, further leading to a lower standard of living and heightened poverty levels.

The relationship between household consumption expenditure and other economic indicators is intricate. For instance, it has a strong tie to Gross Domestic Product (GDP); increased consumption often translates directly into GDP growth. Additionally, it can influence measures like the unemployment rate: when households spend more, businesses experience heightened demand, leading to job creation. In contrast, when spending declines, businesses may lay off workers, resulting in elevated unemployment rates.

Understanding this indicator also involves considering various factors that may influence it. Economic policies, inflation rates, interest rates, and the overall economic environment can heavily affect consumption patterns. For example, high inflation can erode purchasing power, causing households to limit expenditures. Conversely, favorable interest rates might encourage people to borrow and spend more, fueling economic growth.

To address negative trends in household and NPISHs’ final consumption expenditure, several strategies can be implemented. Economic stimulus measures, such as tax cuts or direct financial assistance to families, may stimulate spending by improving disposable income. Additionally, enhancing consumer confidence is vital; governments might engage in public campaigns detailing economic recovery efforts or invest in infrastructure to create jobs and bolster optimism.

It is also crucial to consider potential flaws in this metric. For instance, while it reflects consumer spending behaviors, it may not account for disparities in consumption among different income groups within households. Thus, an overall growth in expenditure could obscure significant inequalities, failing to capture the experiences of lower-income households who may struggle with affordability even amid general economic improvement.

Examining historical data from as far back as 1971 sheds light on the volatility of household consumption expenditure growth over the decades. Values ranged from a high of 3.85% in 1972 to a low of -14.45% in Madagascar in 2023. This showcases significant fluctuations reflecting global events, economic booms and busts, and inflation rates that have reshaped consumer behavior over time. For example, from the late 2000s into the early 2020s, we see a trend where values dipped significantly during the financial crisis and were negatively impacted during the COVID-19 pandemic, with a historical low of -5.46% in 2020 before rebounding in subsequent years.

The recovery seen in 2021, with a remarkable growth of 6.43%, illustrates how quickly consumer markets can react to effective stimulus measures and changes in economic confidence. Although the growth rates have moderated in 2022 and into 2023, the trajectory suggests a gradual return to pre-pandemic norms, albeit with underlying economic challenges that may shape future consumption patterns.

In conclusion, household and NPISH final consumption expenditure per capita growth serves as a pivotal economic indicator illustrating the well-being and financial health of consumers. Understanding its significance, related factors, and the implications of its fluctuations can guide policymakers and economists in fostering conducive economic environments for sustainable growth.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'NE.CON.PRVT.PC.KD.ZG'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'NE.CON.PRVT.PC.KD.ZG'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))